Gresham Lab Flow Core Guide
David
2016-11-02
Experiment overview
Write a detailed description of your experiment here including the goal of the analysis and your interpretation of the results.
If you still see this text it means that you have not described the experiment and whatever follows is meaningless. ###############################
This code is designed for use with the Accuri flow cytometer, which is equiped with the following lasers and filters
- Blue laser (488 nm)
- FL1 filter = 514/20nm GFP
FL3 filter = 575/25nm YFP
- Yellow/green laser (552 nm)
- FL2 filter = 610/20nm mCherry, dtomato
FL4 filter = 586/15nm DsRed
Requirements
In order to run this code you need:
+ to predefine your gates using the gating.R script
+ the gates.Rdata workspace, which contains the gates used in this script
+ the path of the directory(ies), given the variable names dir1, dir2… that contain .fcs files named A01.fcs, A02.fcs, A03.fcs…
+ a tab delimited sample sheet in each directory with the following rows:
+ the variable names are changed in chunk 2 named “Variable Names”
Output
This script generates a summary of results followed by quality control plots.
1 Step 1: Load relevant libraries
# This is a function that just makes sure you have a package, or installs it for you without prompting
requireInstall <- function(packageName,isBioconductor=F) {
if ( !try(require(packageName,character.only=T)) ) {
print(paste0("You don't have ",packageName," accessible, ",
"I'm gonna install it"))
if (isBioconductor) {
source("http://bioconductor.org/biocLite.R")
biocLite(packageName)
} else {
install.packages("packageName", repos = "http://cran.us.r-project.org")
}
}
return(1)
}
#Load libraries
requireInstall("flowCore",isBioconductor=T)## Loading required package: flowCore## [1] 1requireInstall("flowViz",isBioconductor=T)## Loading required package: flowViz## Loading required package: lattice## [1] 1requireInstall("flowStats")## Loading required package: flowStats## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'flowStats'## [1] "You don't have flowStats accessible, I'm gonna install it"## Warning: package 'packageName' is not available (for R version 3.3.2)## [1] 1requireInstall("Hmisc")## Loading required package: Hmisc## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'Hmisc'## [1] "You don't have Hmisc accessible, I'm gonna install it"## Warning: package 'packageName' is not available (for R version 3.3.2)## [1] 1requireInstall("reshape2")## Loading required package: reshape2## [1] 1requireInstall("ggplot2")## Loading required package: ggplot2## [1] 1requireInstall("flowWorkspace")## Loading required package: flowWorkspace## Loading required package: ncdfFlow## Loading required package: RcppArmadillo## Loading required package: BH## [1] 1requireInstall("ggcyto", isBioconductor=T)## Loading required package: ggcyto## [1] 1requireInstall("gridExtra")## Loading required package: gridExtra## [1] 12 Step 2: Read in .fcs files, an Rdata file containing the gates sample sheet(s) that contains four columns with
- column1 = Well
- column2 = Strain
- column3 = Staining
- column4 = Media
- column5 = Userdefined
#Read in all data for analysis. Data should be in individual directories that contain .fcs files and a corresponding sample sheet with a generic format. FCS file names should be unaltered e.g AO1.fcs, A02.fcs, ...H12.fcs
#An abitrary number of directories can be used named dir1, dir2, dir3...with a corresponding flowData.1, flowData.2, flowData.3...and sample.sheet.1, sample.sheet.2, sample.sheet.3...
#load the Rdata file containing the gates
load("gates.Rdata")
#Define the directory, or directories, containing your .fcs files using absolute path names
dir1 <- "/Users/David/Google Drive/Gresham Lab_David/flow/flow cytometry"
dir2 <- "/Users/David/Google Drive/Gresham Lab_David/flow/flow cytometry"
#Read in all the fcs files in the directory, with alter.names changing "-" to "."
flowData.1 <- read.flowSet(path = dir1, pattern=".fcs", alter.names=TRUE)
flowData.2 <- read.flowSet(path = dir2, pattern=".fcs", alter.names=TRUE)
#Read in the sample sheet that should be in each directory that contains the .fcs files.
sample.sheet.1 <- read.delim(paste(dir1, "SampleSheet.txt", sep="/"))
sample.sheet.2 <- read.delim(paste(dir2, "SampleSheet2.txt", sep="/"))
#Change names of samples to those specified in the sample sheets
sampleNames(flowData.1) <- paste(sample.sheet.1[,1], sample.sheet.1[,2], sample.sheet.1[,3], sample.sheet.1[,4], sample.sheet.1[,5], sample.sheet.1[,6], sep=" ")
sampleNames(flowData.2) <- paste(sample.sheet.2[,1], sample.sheet.2[,2], sample.sheet.2[,3], sample.sheet.2[,4], sample.sheet.2[,5], sample.sheet.2[,6], sep=" ")#Check how many cells were counted in each fcs file
fsApply(flowData.1, each_col, length)[1:6]## [1] 50000 50000 50000 50000 50000 50000fsApply(flowData.2, each_col, length)[1:6]## [1] 50000 50000 50000 50000 50000 50000total.1 <- fsApply(flowData.1, each_col, length)[1:6] #total counts per sample
total.2 <- fsApply(flowData.1, each_col, length)[1:6] #total counts per sample
#Print the medians of data values for each measurement
fsApply(flowData.1, each_col, median)## FSC.A SSC.A FL1.A FL2.A
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 562697.0 77701.0 2566.0 276
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 637355.5 90199.5 9941.0 558
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 579237.0 79793.0 19631.0 1146
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 546466.0 80743.0 20697.5 1267
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 552799.5 81623.0 25457.0 1641
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 524191.5 78656.0 28496.5 1862
## FL3.A FL4.A FSC.H SSC.H
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 756 251 871409.5 108347.0
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 2242 493 969225.0 122714.5
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 4984 1002 898238.0 110155.0
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 5452 1090 852802.0 113254.0
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 7192 1423 859692.0 114994.5
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 8128 1614 820285.5 111785.0
## FL1.H FL2.H FL3.H FL4.H Width
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 2846.0 185 1594 421 58
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 9728.0 419 2453 568 60
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 18645.0 953 4677 959 58
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 19810.0 1077 5120 1042 57
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 24078.0 1412 6609 1328 58
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 27025.5 1616 7456 1508 57
## Time
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 184
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 184
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 188
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 184
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 186
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 188fsApply(flowData.2, each_col, median)## FSC.A SSC.A FL1.A FL2.A
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 562697.0 77701.0 2566.0 276
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 637355.5 90199.5 9941.0 558
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 579237.0 79793.0 19631.0 1146
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 546466.0 80743.0 20697.5 1267
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 552799.5 81623.0 25457.0 1641
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 524191.5 78656.0 28496.5 1862
## FL3.A FL4.A FSC.H SSC.H
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 756 251 871409.5 108347.0
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 2242 493 969225.0 122714.5
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 4984 1002 898238.0 110155.0
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 5452 1090 852802.0 113254.0
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 7192 1423 859692.0 114994.5
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 8128 1614 820285.5 111785.0
## FL1.H FL2.H FL3.H FL4.H Width
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 2846.0 185 1594 421 58
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 9728.0 419 2453 568 60
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 18645.0 953 4677 959 58
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 19810.0 1077 5120 1042 57
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 24078.0 1412 6609 1328 58
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 27025.5 1616 7456 1508 57
## Time
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 184
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 184
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 188
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 184
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 186
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 188#combine all flowSets into a single flowset
flowData <- rbind2(flowData.1, flowData.2)
total <- fsApply(flowData, each_col, length)[1:12] #total number of measurements per sample
fsApply(flowData, each_col, median)## FSC.A SSC.A FL1.A FL2.A
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 562697.0 77701.0 2566.0 276
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 637355.5 90199.5 9941.0 558
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 579237.0 79793.0 19631.0 1146
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 546466.0 80743.0 20697.5 1267
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 552799.5 81623.0 25457.0 1641
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 524191.5 78656.0 28496.5 1862
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 562697.0 77701.0 2566.0 276
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 637355.5 90199.5 9941.0 558
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 579237.0 79793.0 19631.0 1146
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 546466.0 80743.0 20697.5 1267
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 552799.5 81623.0 25457.0 1641
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 524191.5 78656.0 28496.5 1862
## FL3.A FL4.A FSC.H SSC.H
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 756 251 871409.5 108347.0
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 2242 493 969225.0 122714.5
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 4984 1002 898238.0 110155.0
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 5452 1090 852802.0 113254.0
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 7192 1423 859692.0 114994.5
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 8128 1614 820285.5 111785.0
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 756 251 871409.5 108347.0
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 2242 493 969225.0 122714.5
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 4984 1002 898238.0 110155.0
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 5452 1090 852802.0 113254.0
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 7192 1423 859692.0 114994.5
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 8128 1614 820285.5 111785.0
## FL1.H FL2.H FL3.H FL4.H Width
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 2846.0 185 1594 421 58
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 9728.0 419 2453 568 60
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 18645.0 953 4677 959 58
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 19810.0 1077 5120 1042 57
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 24078.0 1412 6609 1328 58
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 27025.5 1616 7456 1508 57
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 2846.0 185 1594 421 58
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 9728.0 419 2453 568 60
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 18645.0 953 4677 959 58
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 19810.0 1077 5120 1042 57
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 24078.0 1412 6609 1328 58
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 27025.5 1616 7456 1508 57
## Time
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 184
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 184
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 188
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 184
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 186
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 188
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 184
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 184
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 188
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 184
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 186
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 188samples.num <- length(flowData)3 Step 3: apply filters to data and generate plots showing the effect on filtering
##Subset the data by applying sequential gates##
#apply doublet gate
flowData.singlets <- Subset(flowData, pg.singlets)
fsApply(flowData.singlets, each_col, length)[1:samples.num]## [1] 42759 41726 42816 42973 42303 42593 42759 41726 42816 42973 42303
## [12] 42593singlets <- fsApply(flowData.singlets, each_col, length)[1:samples.num]
barplot(singlets/total, ylim=c(0,1), ylab = "Proportion singlet cells", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#apply debris gate
filteredData <- Subset(flowData.singlets, pg.nondebris)
fsApply(filteredData, each_col, length)[1:samples.num]## [1] 42204 41196 42282 42112 41538 41535 42204 41196 42282 42112 41538
## [12] 41535non.debris <- fsApply(filteredData, each_col, length)[1:samples.num]
barplot(non.debris/total, ylim=c(0,1), ylab = "Proportion singlet and nondebris cells", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#########
#filteredData is the variable name for the data filtered of doublets and debris that are used for all subsequent analyses
##########
#this gate defines nongfp cells
gfp.neg <- Subset(filteredData, pg.nongfp)
fsApply(gfp.neg, each_col, length)[1:samples.num]## [1] 41827 7876 133 214 13 7 41827 7876 133 214 13
## [12] 7non.gfp <- fsApply(gfp.neg, each_col, length)[1:samples.num]
barplot(non.gfp/non.debris, ylim=c(0,1), ylab = "Proportion cells with no GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#this gate defines gfp cells
gfp.pos <- Subset(filteredData, pg.gfp)
fsApply(gfp.pos, each_col, length)[1:samples.num]## [1] 12412 40040 33378 26725 14695 6472 12412 40040 33378 26725 14695
## [12] 6472gfp.cells <- fsApply(gfp.pos, each_col, length)[1:samples.num]
barplot(gfp.cells/non.debris, ylim=c(0,1), ylab = "Proportion cells with GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#this gate defines high GFP cells
gfp.hi <- Subset(filteredData, pg.hi.gfp)
fsApply(gfp.hi, each_col, length)[1:samples.num]## [1] 0 3407 37121 37752 39674 39601 0 3407 37121 37752 39674
## [12] 39601hi.gfp.cells <- fsApply(gfp.hi, each_col, length)[1:samples.num]
barplot(hi.gfp.cells/non.debris, ylim=c(0,1), ylab = "Proportion cells with high GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
4 Step 4: Data analysis
4.1 diagnostic values can be defined for plotting purposes
#define critical values that can superimposed on plots for easy visual comparison
gfp.bg <- 3.9 #a background value for GFP
gfp.wt <- 5.9 #a value for wildtype GFP expression
red.bg <- 3.03 #a background value for the red channel
red.wt <- 3.75 #a value for wildtype Red expression
haploid.fsc <- 6e5 #an empirical value for forward scatter for haploids
diploid.fsc <- 7e5 #an empirical value for forward scatter for diploids
gfp.norm <- 0.935 #an empricial value for gfp expression normalized by forward scatter
red.norm <- 0.57 #an empricial value for red expression normalized by forward scatter
gfp.red.norm <- 1.5 #an empricial value for gfp expression normalized by red channel4.2 Extract data from fcs files to generate statistics and boxplots
#record summary statistics for each sample in a matrix named summary.stats
summary.stats <- matrix(data = NA, nrow = length(filteredData), ncol = 18, dimnames = list(sampleNames(filteredData),c("FSC_median","FSC_mean", "FSC_sd","FL1_median", "FL1_mean","FL1_sd","normalizedGFP_median", "normalizedGFP_mean", "normalizedGFP_sd","FL2_median","FL2_mean","FL2_sd","normalizedRed_median","normalizedRed_mean", "normalizedRed_sd","GFPnormalizedByRed_median", "GFPnormalizedByRed_mean","GFPnormalizedByRed_sd")))
#use the sample containing the minimum number of points after filtering for doublets and debris to define the number of data points retained for all samples
sample.size <- min(fsApply(filteredData, each_col, length))
print(sample.size)## [1] 41196comparison.FSC <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL2 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1NormFsc <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL2NormFsc <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1NormFL2 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
#for each sample plot a histogram of the normalized data, raw FSC and raw GFP per row
par(mfrow=c(1,2), mar=c(5.1,2.1,2.1,2.1), oma=c(1.5,2,1,1))
#extract data from flowFrames to plot histograms of values and record summary statistics
for (i in 1:length(filteredData)){
temp <- exprs(filteredData[[i]]) #exprs() extracts a matrix of the values from the flowframe
##########################################
#record summary statistics for the sample#
##########################################
#FSC
summary.stats[i,1] <- median(temp[,1])
summary.stats[i,2] <-mean(temp[,1])
summary.stats[i,3] <- sd(temp[,1])
#FL1
summary.stats[i,4] <- median(temp[,3])
summary.stats[i,5] <-mean(temp[,3])
summary.stats[i,6] <- sd(temp[,3])
#FL1 (GFP) divided by FSC
summary.stats[i,7] <- median(temp[,3]/temp[,1])
summary.stats[i,8] <-mean(temp[,3]/temp[,1])
summary.stats[i,9] <- sd(temp[,3]/temp[,1])
#FL2
summary.stats[i,10] <- median(temp[,4])
summary.stats[i,11] <-mean(temp[,4])
summary.stats[i,12] <- sd(temp[,4])
#FL2 (Red) divided by FSC
summary.stats[i,13] <- median(temp[,4]/temp[,1])
summary.stats[i,14] <-mean(temp[,4]/temp[,1])
summary.stats[i,15] <- sd(temp[,4]/temp[,1])
#FL1 (GFP) divided by FL2 (Red)
summary.stats[i,16] <- median(temp[,3]/temp[,4])
summary.stats[i,17] <-mean(temp[,3]/temp[,4])
summary.stats[i,18] <- sd(temp[,3]/temp[,4])
##############################################
#plot histograms of the channels of interest##
##############################################
###############
#Green channel#
###############
#FL1 (GFP)
hist(log10(temp[,3]), br=1000, xlab = "log10(FL1)", main = "FL1")
abline(v=gfp.bg, col="yellow", lty=2, lwd=2)
abline(v=gfp.wt, col="green", lty=2, lwd=2)
legend("topleft", legend=paste("median FL1 = ",round(median(temp[,3]), digits=4),sep=""))
#GFP divided by FSC
hist(temp[,3]/temp[,1], br=500, xlab = "FL1/FSC", main = "FL1/FSC")
abline(v=gfp.norm, col="green", lty=2, lwd=2 )
legend("topleft", legend=paste("median GFP / FSC=",round(median(temp[,3]/temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
###############
#Red channel#
###############
#FL2 (Red)
hist(log10(temp[,4]), br=500, xlab = "log10(FL2)", main = "FL2")
abline(v=red.bg, col="yellow", lty=2, lwd=2)
abline(v=red.wt, col="red", lty=2, lwd=2)
legend("topleft", legend=paste("median FL2=",round(median(temp[,4]), digits=4),sep=""))
#FL2 divided by FSC
hist(temp[,4]/temp[,1], br=500, xlab = "FL2/FSC", main = "FL2/FSC")
abline(v=red.norm, col="red", lty=2, lwd=2 )
legend("topleft", legend=paste("median FL2 / FSC=",round(median(temp[,4]/temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
###############
#Other#########
###############
#FL1 divided by FL2
hist(temp[,4]/temp[,3], br=500, xlab = "FL2/FL1", main = "FL1/FL2")
abline(v=gfp.red.norm, col="purple", lty=2, lwd=2)
legend("topleft", legend=paste("median FL1 / FL2=",round(median(temp[,4]/temp[,3]), digits=4),sep=""))
#FSC
hist(log10(temp[,1]), br=500, xlab = "log10(FSC)", main = "FSC", xlim=c(4,8))
abline(v=haploid.fsc, col="blue", lty=2, lwd=2)
abline(v=diploid.fsc, col="grey", lty=2, lwd=2)
legend("topleft", legend=paste("median FSC=",round(median(temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
print("-------------------------------------------------------")
print("-----------------------------------")
print("----------------------")
############################################################
#keep the data set for generating boxplots comparing values#
############################################################
#Note that the amount of data kept for each sample is defined by the lowest count among all the samples.
comparison.FSC[1:sample.size,i] <- temp[1:sample.size,1] #FSC
comparison.FL1[1:sample.size,i] <- temp[1:sample.size,3] #FL1 (GFP)
comparison.FL1NormFsc[1:sample.size,i] <- temp[1:sample.size,3]/temp[1:sample.size,1] #GFP/FSC
comparison.FL2[1:sample.size,i] <- temp[1:sample.size,4] #FL2
comparison.FL2NormFsc[1:sample.size,i] <- temp[1:sample.size,4]/temp[1:sample.size,1] #FL2/FSC
comparison.FL1NormFL2[1:sample.size,i] <- temp[1:sample.size,3]/temp[1:sample.size,4] #FL1/FL2
}
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## [1] "----------------------"par(mfrow=c(1,1)) #change number of plots per row back to standard4.3 Overview of data distributions
par(mar=c(8.1,4.1,4.1,2.1)) #create more space at lower margin
boxplot(comparison.FSC, names=sampleNames(filteredData), notch = TRUE, col = "gray", ylab="FSC", cex.axis=0.5,las=2, outline=F)
abline(h=haploid.fsc, lty=2, col=2)
abline(h=diploid.fsc, lty=2, col=3)
boxplot(comparison.FL1, names=sampleNames(filteredData), notch = TRUE, col = "lightgreen", ylab="FL1", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.bg ,lty=2, lwd=3, col="yellow")
abline(h=gfp.wt, lty = 2, lwd=3, col="green")
boxplot(comparison.FL1NormFsc, names=sampleNames(filteredData), notch = TRUE, col = "green", ylab="FL1/FSC", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.norm, lty=2, lwd=3, col="blue")
boxplot(comparison.FL2, names=sampleNames(filteredData), notch = TRUE, col = "pink", ylab="FL2", cex.axis=0.5,las=2, outline=F)
abline(h=red.bg, lty=2, lwd=3, col="pink")
abline(h=red.wt, lty=2, lwd=3, col="red")
boxplot(comparison.FL2NormFsc, names=sampleNames(filteredData), notch = TRUE, col = "red", ylab="FL2/FSC", cex.axis=0.5,las=2, outline=F)
abline(h=red.norm, lty=2, lwd=3, col="red")
boxplot(comparison.FL1NormFL2, names=sampleNames(filteredData), notch = TRUE, col = "purple", ylab="FL1/FL2", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.red.norm, lty=2, lwd=3, col="purple")
par(mar=c(5.1,4.1,4.1,2.1)) #reset margins to default
#generate a summary table containing all the recorded statistics
print(summary.stats)## FSC_median FSC_mean FSC_sd
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 550362.0 578443.4 182901.5
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 619717.0 646434.3 196374.5
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 566471.5 594382.6 186745.5
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 534210.0 565948.5 185732.2
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 541182.0 570647.5 182383.9
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 515137.0 545937.4 179056.6
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 550362.0 578443.4 182901.5
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 619717.0 646434.3 196374.5
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 566471.5 594382.6 186745.5
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 534210.0 565948.5 185732.2
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 541182.0 570647.5 182383.9
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 515137.0 545937.4 179056.6
## FL1_median FL1_mean FL1_sd
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 2389.0 2505.798 1432.759
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 9261.0 13525.881 49008.480
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 18448.5 39403.262 186594.043
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 19512.0 43152.557 206930.543
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 23913.0 55436.518 253074.807
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 26849.0 63747.223 285618.143
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 2389.0 2505.798 1432.759
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 9261.0 13525.881 49008.480
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 18448.5 39403.262 186594.043
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 19512.0 43152.557 206930.543
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 23913.0 55436.518 253074.807
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 26849.0 63747.223 285618.143
## normalizedGFP_median
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 0.004273229
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 0.014567241
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 0.032165375
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 0.036165830
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 0.043614604
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 0.051061943
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 0.004273229
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 0.014567241
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 0.032165375
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 0.036165830
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 0.043614604
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 0.051061943
## normalizedGFP_mean
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 0.004358658
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 0.022134494
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 0.069675151
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 0.079291972
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 0.101594779
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 0.121021319
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 0.004358658
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 0.022134494
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 0.069675151
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 0.079291972
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 0.101594779
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 0.121021319
## normalizedGFP_sd FL2_median
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 0.002265344 266
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 0.093144413 525
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 0.337765302 1084
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 0.379927453 1205
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 0.462591869 1548
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 0.535092454 1760
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 0.002265344 266
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 0.093144413 525
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 0.337765302 1084
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 0.379927453 1205
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 0.462591869 1548
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 0.535092454 1760
## FL2_mean FL2_sd
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 286.1407 219.7696
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 697.4944 2189.1242
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 2113.9327 9459.2402
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 2467.8085 11441.0674
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 3389.7610 15059.6897
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 4121.0068 18461.7822
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 286.1407 219.7696
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 697.4944 2189.1242
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 2113.9327 9459.2402
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 2467.8085 11441.0674
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 3389.7610 15059.6897
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 4121.0068 18461.7822
## normalizedRed_median
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 0.0004734632
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 0.0008324645
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 0.0018947655
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 0.0022309944
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 0.0028458199
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 0.0033833915
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 0.0004734632
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 0.0008324645
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 0.0018947655
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 0.0022309944
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 0.0028458199
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 0.0033833915
## normalizedRed_mean
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 0.0005225371
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 0.0011504355
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 0.0037717334
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 0.0045655837
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 0.0062481366
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 0.0078667444
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 0.0005225371
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 0.0011504355
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 0.0037717334
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 0.0045655837
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 0.0062481366
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 0.0078667444
## normalizedRed_sd
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 0.000419210
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 0.004240531
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 0.017446809
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 0.021139278
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 0.027725480
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 0.034954035
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 0.000419210
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 0.004240531
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 0.017446809
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 0.021139278
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 0.027725480
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 0.034954035
## GFPnormalizedByRed_median
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 NA
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 17.98301
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 17.13975
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 16.36705
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 15.55815
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 15.31553
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 NA
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 17.98301
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 17.13975
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 16.36705
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 15.55815
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 15.31553
## GFPnormalizedByRed_mean
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 NaN
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 Inf
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 Inf
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 Inf
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 Inf
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 Inf
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 NaN
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 Inf
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 Inf
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 Inf
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 Inf
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 Inf
## GFPnormalizedByRed_sd
## A01 DGY1 no_stain 1N C-lim-D0.3 expt1 NA
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt1 NaN
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt1 NaN
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt1 NaN
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt1 NaN
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt1 NaN
## A01 DGY1 no_stain 1N C-lim-D0.3 expt2 NA
## A02 DGY1 10uL_500nM 1N C-lim-D0.3 expt2 NaN
## A03 DGY1 50uL_500nM 1N C-lim-D0.3 expt2 NaN
## A04 DGY1 100uL_500nM 1N C-lim-D0.3 expt2 NaN
## A05 DGY1 250uL_500nM 1N C-lim-D0.3 expt2 NaN
## A06 DGY1 500uL_500nM 1N C-lim-D0.3 expt2 NaNsummary.stats <- as.data.frame(summary.stats)4.4 Quantitation of relative FL1 signal
baseline.FL1 <- summary.stats$FL1_median[1]
barplot(summary.stats$FL1_median/baseline.FL1, ylab="Relative FL1 median expression", las=2, cex.names = 0.5, names.arg=sampleNames(filteredData))
4.5 Quantitation of forward scatter
baseline.FSC <- summary.stats$FSC_median[1]
barplot(summary.stats$FSC_median/baseline.FSC, ylab="Relative median FSC", las=2, cex.names = 0.5, names.arg=sampleNames(filteredData))
4.6 Population composition
pop.composition <- rbind(non.gfp/non.debris,gfp.cells/non.debris,hi.gfp.cells/non.debris)
barplot(pop.composition, ylab="Proportion of population", legend=c("No GFP", "Normal GFP", "High GFP"),las=2, cex.names = 0.5,names.arg=sampleNames(filteredData))
5 Step 5: Quality control
5.1 Gates
###First flowset
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData.1, xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - singlets gate")
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData.1, displayFilter=TRUE, xlim=c(0,3e6), ylim=c(0,3e5), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - nondebris gate")
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.1, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - non GFP gate")
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.1, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - GFP gate")
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData.1, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - high GFP gate")
################
###Second flowset
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData.2, xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - singlets gate")
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData.2, displayFilter=TRUE, xlim=c(0,3e6), ylim=c(0,3e5), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - nondebris gate")
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.2, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - non GFP gate")
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.2, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - GFP gate")
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData.2, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - high GFP gate")
#####Attempted as loop below to plot each one individually and is not working
for (i in 1:length(filteredData)){
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData[i], xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData[i], displayFilter=TRUE, xlim=c(0,3e5), ylim=c(0,3e6), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData[i], displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData[i], displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData[i], xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
}5.2 Data transformation for visualization
#In order to look at QC plots the data is transformed using the logicle transform, which is a log transform for high values that transitions to a linear transformation near zero values
#This is simply for visualization purposes
lgcl <- logicleTransform(w = 0.5, t= 10000, m=4.5) #the parameters w,t, and m define the transformation parameters
#Dataset 1 tranformation applied to every channel except width and time
dataLGCLTransform <- transform(filteredData,'FSC.A' = lgcl(`FSC.A`), 'SSC.A' =lgcl(`SSC.A`), 'FL1.A' = lgcl(`FL1.A`), 'FL2.A' = lgcl(`FL2.A`), 'FL3.A' = lgcl(`FL3.A`), 'FL4.A' = lgcl(`FL4.A`),'FSC.H' = lgcl(`FSC.H`),'SSC.H' = lgcl(`SSC.H`),'FL1.H' = lgcl(`FL1.H`),'FL2.H' = lgcl(`FL2.H`),'FL3.H' = lgcl(`FL3.H`),'FL4.H' = lgcl(`FL4.H`)) 5.3 Effect of time
#The effect of time on signal (of which there shouldn't be any)
i <- 1
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
i <- 2
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
i <- 3
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
}5.4 Plots of FL1 versus FSC
i <- 1
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 2
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 3
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(2,6), sampleNames(filteredData)[i])
}5.5 Plots of FSC versus SSC
i <- 1
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 2
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 3
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])
}